PPARγ Ligand–induced Annexin A1 Expression Determines Chemotherapy Response via Deubiquitination of Death Domain Kinase RIP in Triple-negative Breast Cancers
Bibliographic record
Abstract
Abstract Metastatic breast cancer is still incurable so far; new specifically targeted and more effective therapies for triple-negative breast cancer (TNBC) are required in the clinic. In this study, our clinical data have established that basal and claudin-low subtypes of breast cancer (TNBC types) express significantly higher levels of Annexin A1 (ANXA1) with poor survival outcomes. Using human cancer cell lines that model the TNBC subtype, we observed a strong positive correlation between expression of ANXA1 and PPARγ. A similar correlation between these two markers was also established in our clinical breast cancer patients' specimens. To establish a link between these two markers in TNBC, we show de novo expression of ANXA1 is induced by activation of PPARγ both in vitro and in vivo and it has a predictive value in determining chemosensitivity to PPARγ ligands. Mechanistically, we show for the first time PPARγ-induced ANXA1 protein directly interacts with receptor interacting protein-1 (RIP1), promoting its deubiquitination and thereby activating the caspase-8–dependent death pathway. We further identified this underlying mechanism also involved a PPARγ-induced ANXA1-dependent autoubiquitination of cIAP1, the direct E3 ligase of RIP1, shifting cIAP1 toward proteosomal degradation. Collectively, our study provides first insight for the suitability of using drug-induced expression of ANXA1 as a new player in RIP1-induced death machinery in TNBCs, presenting itself both as an inclusion criterion for patient selection and surrogate marker for drug response in future PPARγ chemotherapy trials. Mol Cancer Ther; 16(11); 2528–42. ©2017 AACR.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".